





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_UpperCamelCase							=			"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

_UpperCamelCase							=			"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

_UpperCamelCase							=			"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def    lowerCAmelCase__(						lowercase							:     Tuple ,  lowercase							:     Dict					)			->     int:
  return float((preds == labels).mean()					)







def    lowerCAmelCase__(						lowercase							:     str ,  lowercase							:     Dict ,  lowercase							:     Tuple="binary"					)			->     Dict:
  __snake_case :					str          =							simple_accuracy(lowercase ,  lowercase					)
  __snake_case :					Dict          =							float(fa_score(y_true=lowercase ,  y_pred=lowercase ,  average=lowercase					)					)
  return {
      "accuracy": acc,
      "f1": fa,
  }







def    lowerCAmelCase__(						lowercase							:     Dict ,  lowercase							:     Tuple					)			->     List[str]:
  __snake_case :					Optional[int]          =							{}
  for id_pred, label in zip(lowercase ,  lowercase					):
    __snake_case :					List[Any]          =							f"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}"""
    __snake_case :					Dict          =							id_pred["prediction"]
    if question_id in question_map:
      question_map[question_id].append((pred, label)					)
    else:
      __snake_case :					Tuple          =							[(pred, label)]
  __snake_case     , __snake_case :					Any          =							[], []
  for question, preds_labels in question_map.items():
    __snake_case     , __snake_case :					List[Any]          =							zip(*lowercase					)
    __snake_case :					int          =							fa_score(y_true=lowercase ,  y_pred=lowercase ,  average="macro"					)
    fas.append(lowercase					)
    __snake_case :					List[Any]          =							int(sum(pred == label for pred, label in preds_labels					) == len(lowercase					)					)
    ems.append(lowercase					)
  __snake_case :					Optional[Any]          =							float(sum(lowercase					) / len(lowercase					)					)
  __snake_case :					List[str]          =							sum(lowercase					) / len(lowercase					)
  __snake_case :					Optional[int]          =							float(fa_score(y_true=lowercase ,  y_pred=[id_pred["prediction"] for id_pred in ids_preds]					)					)
  return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			, _KWARGS_DESCRIPTION     )
class 					_lowerCamelCase			(     datasets.Metric     ):



      """simple docstring"""






      def     UpperCAmelCase		(			self )							-> Dict:






        '''simple docstring'''






        if self.config_name not in [
            "boolq",
            "cb",
            "copa",
            "multirc",
            "record",
            "rte",
            "wic",
            "wsc",
            "wsc.fixed",
            "axb",
            "axg",
        ]:
          raise KeyError(
              "You should supply a configuration name selected in "
              "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
        return datasets.MetricInfo(
            description=_DESCRIPTION					,    citation=_CITATION					,    inputs_description=_KWARGS_DESCRIPTION					,    features=datasets.Features(self._get_feature_types() )					,    codebase_urls=[]					,    reference_urls=[]					,    format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None					,    )







      def     UpperCAmelCase		(			self )							-> Optional[Any]:






        '''simple docstring'''






        if self.config_name == "record":
          return {
              "predictions": {
                  "idx": {
                      "passage": datasets.Value("int64" ),
                      "query": datasets.Value("int64" ),
                  },
                  "prediction_text": datasets.Value("string" ),
              },
              "references": {
                  "idx": {
                      "passage": datasets.Value("int64" ),
                      "query": datasets.Value("int64" ),
                  },
                  "answers": datasets.Sequence(datasets.Value("string" ) ),
              },
          }
        elif self.config_name == "multirc":
          return {
              "predictions": {
                  "idx": {
                      "answer": datasets.Value("int64" ),
                      "paragraph": datasets.Value("int64" ),
                      "question": datasets.Value("int64" ),
                  },
                  "prediction": datasets.Value("int64" ),
              },
              "references": datasets.Value("int64" ),
          }
        else:
          return {
              "predictions": datasets.Value("int64" ),
              "references": datasets.Value("int64" ),
          }







      def     UpperCAmelCase		(			self					,    UpperCAmelCase					,    UpperCAmelCase )							-> Union[str, Any]:






        '''simple docstring'''






        if self.config_name == "axb":
          return {"matthews_correlation": matthews_corrcoef(UpperCAmelCase					,    UpperCAmelCase )}
        elif self.config_name == "cb":
          return acc_and_fa(UpperCAmelCase					,    UpperCAmelCase					,    fa_avg="macro" )
        elif self.config_name == "record":
          __snake_case :					Dict          =							[
              {
                  "qas": [
                      {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
                      for ref in references
                  ]
              }
          ]
          __snake_case :					Tuple          =							{pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
          return evaluate_record(UpperCAmelCase					,    UpperCAmelCase )[0]
        elif self.config_name == "multirc":
          return evaluate_multirc(UpperCAmelCase					,    UpperCAmelCase )
        elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
          return {"accuracy": simple_accuracy(UpperCAmelCase					,    UpperCAmelCase )}
        else:
          raise KeyError(
              "You should supply a configuration name selected in "
              "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )


"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__:Dict									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use DonutImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
